Bladder cancer is a common type of cancer that can cause substantial morbidity and mortality among both men and women. Bladder cancer causes over 16,870 deaths per year in the United States with 79,030 new bladder cancer cases diagnosed in 2017. A reliable assessment of the response to neoadjuvant therapy at an early stage is vital for identifying tumors that do not respond and allowing the patient a chance of alternative treatment. We have successfully developed a computer decision support system (CDSS-T) for monitoring of bladder cancer treatment response. A quantitative image analysis tool for bladder cancer (QIBC) that quantifies the bladder gross tumor volume (GTV) and image characteristics is an important component of CDSS-T. Advanced machine learning techniques are used to merge the GTV and radiomic biomarkers into an effective predictive model. The goal of this project is to validate the effectiveness of CDSS-T as an aid to the radiologists and the oncologists in assessment of bladder cancer change as a result of treatment through pilot clinical trials. We will (1) perform a preparatory clinical trial with the clinicians at UM, which will simulate the real prospective clinical trial with high quality retrospective data, (2) deploy the QIBC and CDSS-T tools at the three collaborating clinical sites, (3) use the QIBC and CDSS-T tools at the different clinical sites in the prospective pilot clinical trial (standard clinical workflow) utilizing the decision support in parallel to the standard clinical patient care, and (4) analyze the impact of the QIBC and CDSS-T tools on the clinicians' performance in the pilot clinical trial and assess the potential impact on clinical outcome. We hypothesize that this innovative approach can improve clinicians' accuracy, consistency and efficiency in bladder GTV estimation and assessment of treatment response. To test these hypotheses, we will perform the following specific tasks: (1) to evaluate the performance of the QIBC and CDSS-T tools on cases not previously used, new to the system, for both the prediction accuracy and the automatic standalone functionality, refine and optimize the design of the user interface based on clinicians' feedback after their hands-on experience with the system to ensure its practicality and robustness, familiarize clinicians with the performance of the CDSS-T tools and the interpretation of the CDSS-T outputs as a part of user training for the prospective pilot clinical trial, (2) to optimize the QIBC and CDSS-T tools for the clinical workflow at the different clinical sites based on the site clinicians' feedback in order to operate efficiently and in a standalone mode by clinicians, (3) to record the clinicians' predicted outcomes without and with the use of the tools during the pilot clinical trials, estimate the accuracy of assessing response to neoadjuvant chemotherapy in the current clinical treatment paradigm by comparing the estimated response to the histopathologically determined response after radical cystectomy, and (4) to statistically analyze the impact of the QIBC and CDSS-T tools on the performance of the clinicians in the pilot clinical trial and statistically assess the potential impact on clinical outcome.